GLIOMATCH: The malignant Glioma immuno-oncology matchmaker: towards data-driven precision medicine using spatially resolved radio-multiomics

GLIOMATCH：恶性胶质瘤免疫肿瘤学媒人：利用空间分辨的放射多组学实现数据驱动的精准医学

• 批准号: 10113516
• 金额: $ 65.57万
• 依托单位: UNIVERSITY OF EDINBURGH
• 依托单位国家: 英国
• 项目类别: EU-Funded
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=10113516
• 关键词: GLIOMATCH; malignant; Glioma; immuno; oncology

Adult and paediatric malignant glioma (GBM and pHGG) remain among the most difficult-to-treat cancers with 5-year survival ratesof <5% despite intensive standard-of-care therapy. The differences among patients and the heterogeneous and plastic nature of eachindividual tumour have resulted in all therapeutic clinical trials failing during the past 20 years. Recently, immunotherapy has beenshowing great promise, but only in subsets of patients. Identifying those patients cannot be done a priori as biomarkers are still largelymissing, nor are we able to follow-up on therapeutic efficacy when patients get treated. The GLIOMATCH project aims at improvingthe clinical outcome of GBM/pHGG patients by enabling immunology-based patient stratification to empower personalised matchingof appropriate immunotherapy, while improving follow-up of clinical responses to existing/novel therapeutics. This will be achievedby integrating spatially resolved, multi-layered tissue maps (using integrated single-cell multiomics), with non-invasive MRI images.This integration will fuel into a novel MRI Radio-multiomics hub, that will be made available to clinical professionals through whichthey can perform tumour-host based patient stratification and personalised therapy matching while interpreting longitudinal follow-upand treatment efficacy. The proposed data-driven models will be developed by analysing the largest cohort of immuno-oncology (I/O)treated GBM/pHGG patients (n>300, including pre-post treatment samples) with matched controls (n>300) and exceptionally long-termsurviving GBM patients (n~140), in which various tumour-host niches will be studied in how they respond to I/O perturbations andlead to improved clinical outcome. This will be empowered by deploying an UNCAN-compatible data lake, to which incremental datacollection will be used to further refine the machine learning models, while proposing novel treatment options. This action is part of theCancer Mission cluster of projects on “Understanding (tumour-host interactions)".

成人和儿童恶性胶质瘤(GBM和PHGG)仍然是最难治疗的癌症之一，尽管进行了强化的标准治疗，但5年存活率仍为5%。患者之间的差异以及每个肿瘤的异质性和可塑性导致了过去20年来所有治疗临床试验的失败。最近，免疫疗法已经显示出巨大的前景，但只适用于部分患者。识别这些患者不能先验，因为生物标记物仍然在很大程度上缺失，我们也无法在患者接受治疗时跟踪观察治疗效果。GLIOMATCH项目旨在改善GBM/PHGG患者的临床结果，方法是基于免疫学的患者分层，使适当的免疫治疗的个性化匹配成为可能，同时改善对现有/新疗法的临床反应的跟进。这将通过将空间分辨率的多层组织图(使用集成的单细胞多细胞组学)与非侵入性MRI图像相结合来实现。这种集成将成为一个新的MRI放射多组学中心，临床专业人员将通过该中心进行基于肿瘤宿主的患者分层和个性化治疗匹配，同时解释纵向随访和治疗效果。建议的数据驱动模型将通过分析免疫肿瘤学(I/O)治疗的GBM/PHGG患者(n&gt；300，包括治疗前样本)与匹配对照组(n&gt；300)和异常长期存活的GBM患者(n~140)的最大队列来开发，其中将研究不同的肿瘤宿主生态位对I/O干扰的反应，并导致临床结果的改善。这将通过部署一个不兼容CAN的数据湖来增强能力，将使用增量数据收集来进一步完善机器学习模型，同时提出新的治疗方案。这项行动是癌症使命“了解(肿瘤-宿主相互作用)”项目集群的一部分。